Emulsified asphalt wheel sticking effect evaluation method, device and medium

Abstract

The application discloses an emulsified asphalt wheel sticking effect evaluation method, equipment and medium, and relates to the technical field of intelligent construction, which comprises the following steps: acquiring a test parameter table, inputting the test parameter table into a microfluidic chip tester for high-throughput testing, and outputting a demulsification kinetics curve and a basic adhesion force data set; combining demulsification process parameters in the demulsification kinetics curve with the basic adhesion force data set to generate a demulsification adhesion coupling data set, constructing a multi-physical field simulation model, and using the demulsification adhesion coupling data set for inversion and calibration to generate a calibrated digital twin model; acquiring construction environment data and inputting the construction environment data into the calibrated digital twin model for automatic multiple simulation calculation to predict wheel sticking risk indexes of different construction schemes; and integrating a construction scheme with the lowest wheel sticking risk index and a safe passing time window to generate an intelligent decision suggestion. The application inverses and calibrates the interfacial energy density through experimental data, and reduces the behavior error of the simulation model and the real material.

Description

Technical Field

[0001] This invention relates to the field of intelligent construction technology, and in particular to a method, equipment and medium for evaluating the adhesion effect of emulsified asphalt to wheels. Background Technology

[0002] In road construction, emulsified asphalt is widely used as an interlayer bonding material in bridge deck paving, road maintenance and other engineering scenarios. The sticking phenomenon during its demulsification and curing process is a key technical problem affecting construction quality. In recent years, with the development of microfluidic technology, researchers have begun to use chip simulation technology to study the behavior of the demulsification interface. They obtain data on the demulsification dynamics process through microscopic observation and mechanical sensors. At the same time, in the field of computational simulation, multiphysics coupling models based on finite element analysis have emerged, which can simulate the process of interface stress coupling due to water evaporation and viscosity changes.

[0003] However, existing technologies have the following problems: traditional material testing methods use isolated parameter measurement modes, which cannot truly reflect the dynamic evolution of adhesion force during demulsification. Although microfluidic technology can achieve process observation, a single experiment can only obtain data under specific working conditions and lacks the ability to systematically analyze the coupling effects of multiple parameters. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for evaluating the adhesion effect of emulsified asphalt, which solves the problem that traditional material testing methods cannot truly reflect the dynamic evolution of adhesion force during demulsification.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for evaluating the wheel adhesion effect of emulsified asphalt, which includes,

[0008] Obtain the test parameter table, input the test parameter table into the microfluidic chip tester for high-throughput testing, and output the demulsification kinetic curve and basic adhesion force dataset;

[0009] The demulsification process parameters in the demulsification kinetic curve are merged with the basic adhesion force dataset to generate a demulsification adhesion coupling dataset. A multiphysics simulation model is constructed and the demulsification adhesion coupling dataset is used for inversion and calibration to generate a calibrated digital twin model.

[0010] The construction environment data is acquired and input into the calibrated digital twin model for automatic multiple simulation calculations to predict the wheel adhesion risk index of different construction schemes;

[0011] The decision algorithm is used to screen the wheel adhesion risk index of different construction schemes, generate the construction scheme with the lowest wheel adhesion risk index, extract the wheel adhesion risk index time series data from the construction scheme with the lowest wheel adhesion risk index, and perform time series analysis on the wheel adhesion risk index time series data to generate a safe passage time window.

[0012] By integrating the construction scheme with the lowest risk of wheel adhesion and the safe passage time window, intelligent decision-making suggestions are generated.

[0013] As a preferred embodiment of the method for evaluating the adhesion effect of emulsified asphalt in this invention, the specific steps for outputting the demulsification kinetics curve and the basic adhesion force dataset are as follows:

[0014] Different types of emulsified asphalt and release agents were obtained and combined to generate a test parameter table;

[0015] Input the test parameter table into the microfluidic chip testing instrument, perform serialized tests according to the order of the test parameter table, and generate time-series images of the demulsification process and adhesion force signals;

[0016] Image processing and analysis are performed on time-series images of the demulsification process to generate demulsification dynamics curves;

[0017] Signal processing and feature extraction are performed on the adhesion force signal to generate a basic adhesion force dataset.

[0018] As a preferred embodiment of the method for evaluating the adhesion effect of emulsified asphalt in this invention, the specific steps for merging the demulsification process parameters in the demulsification kinetic curve with the basic adhesion force dataset to generate a demulsification-adhesion coupling dataset are as follows.

[0019] Curve fitting and parameter extraction were performed on the demulsification kinetics curve to obtain parameters of the demulsification process.

[0020] The demulsification process parameters and the basic adhesion force dataset are correlated and combined to generate a demulsification adhesion coupling dataset.

[0021] As a preferred embodiment of the method for evaluating the adhesion effect of emulsified asphalt in this invention, the specific steps for constructing a multiphysics simulation model and using a demulsification and adhesion coupling dataset for inversion and calibration to generate a calibrated digital twin model are as follows.

[0022] By combining Fick's second law with Navier-Stokes' equations, a multiphysics coupling framework is constructed.

[0023] Geometric modeling and mesh generation were performed on the physical dimensions of the observation cavity of the multiphysics coupling framework and microfluidic chip experimental instrument to generate a multiphysics computational mesh;

[0024] Boundary identification of the physical dimensions of the observation cavity of the microfluidic chip test instrument is performed to obtain boundary conditions. Parameter matching is performed on different types of emulsified asphalt and release agents to obtain initial material parameters.

[0025] Boundary conditions and initial material parameters are applied to the multiphysics coupling framework and the multiphysics computational grid to construct a multiphysics simulation model;

[0026] The demulsification and adhesion coupling dataset is input into the multiphysics simulation model for inversion and calibration, generating a calibrated digital twin model.

[0027] As a preferred embodiment of the method for evaluating the adhesion effect of emulsified asphalt in this invention, the specific steps for predicting the adhesion risk index of different construction schemes are as follows:

[0028] The system acquires temperature, humidity, and traffic waiting time, combines and arranges them to generate a construction plan parameter table, and simultaneously acquires forecast environmental data.

[0029] The construction scheme parameter table and the forecast environmental data are input into the calibrated digital twin model. The calibrated digital twin model automatically traverses all construction schemes in the construction scheme parameter table, performs multiple simulation calculations on the forecast environmental data and all construction schemes, and outputs the wheel adhesion risk index of different construction schemes.

[0030] As a preferred embodiment of the emulsified asphalt wheel adhesion effect evaluation method of the present invention, the specific steps for extracting the wheel adhesion risk index time series data from the construction scheme with the lowest wheel adhesion risk index are as follows:

[0031] The wheel adhesion risk index of different construction schemes is compared and ranked by a sorting algorithm to obtain the construction scheme with the lowest wheel adhesion risk index.

[0032] The construction scheme with the lowest wheel adhesion risk index is indexed and retrieved to generate time series data of wheel adhesion risk index.

[0033] As a preferred embodiment of the emulsified asphalt wheel adhesion effect evaluation method of the present invention, the specific steps for performing time series analysis on the time series data of the wheel adhesion risk index to generate a safe passage time window are as follows.

[0034] The safety threshold for the sticking wheel risk index was set based on historical experimental data;

[0035] Traverse the time series data of the sticky wheel risk index to identify the start time when the sticky wheel risk index first falls below the safe threshold and the end time when it last exceeds the safe threshold.

[0036] The time interval between the start time and the end time is used as the safe passage time window.

[0037] As a preferred embodiment of the emulsified asphalt wheel adhesion effect evaluation method of the present invention, the step of integrating the construction scheme with the lowest wheel adhesion risk index and the safe passage time window to generate intelligent decision suggestions includes the following specific steps.

[0038] The construction scheme with the lowest roller adhesion risk index is analyzed to generate the optimal construction scheme parameter set.

[0039] The optimal construction plan parameter set and safe passage time window are structurally integrated to generate intelligent decision suggestions.

[0040] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the emulsified asphalt sticking wheel effect evaluation method as described in the first aspect of the present invention.

[0041] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for evaluating the adhesion effect of emulsified asphalt wheels as described in the first aspect of the present invention.

[0042] The beneficial effects of this invention are as follows: by synchronously acquiring time-series images and mechanical signals, the holographic quantification of the demulsification process from morphological changes to mechanical responses is realized; through sequential experimental design, the data acquisition efficiency is significantly improved; by inverting and calibrating the interface energy density through experimental data, the behavioral error between the simulation model and the real material is reduced; by automatically traversing all possible construction schemes, the reliability of the scheme is improved; based on the trend analysis of the time series, risk rebound points are identified in advance, enabling proactive prevention and control; and by generating decision suggestions based on specific materials and environment, the consistency of construction quality is improved. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart for evaluating the adhesion effect of emulsified asphalt on wheels.

[0045] Figure 2 This is a flowchart of the microfluidic chip testing instrument.

[0046] Figure 3 A flowchart for constructing a multiphysics simulation model.

[0047] Figure 4 Generate flowcharts for risk prediction and decision-making. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0051] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for evaluating the adhesion effect of emulsified asphalt to wheels, including the following steps:

[0052] S1: Obtain the test parameter table, input the test parameter table into the microfluidic chip tester for high-throughput testing, and output the demulsification kinetic curve and basic adhesion force dataset.

[0053] S1.1: Obtain different types of emulsified asphalt and release agents and combine them to generate a test parameter table.

[0054] Obtain the test material list, read all different types of emulsified asphalt and release agent from the test material list, perform full combination pairing of all different types of emulsified asphalt and release agent to generate several material combination pairs, configure the volume ratio and fluid flow rate for each material combination pair, organize the several material combination pairs and their corresponding volume ratios and fluid flow rates, format them, and generate a test parameter table. The test parameter table clearly specifies the emulsified asphalt type, release agent type, volume ratio, and fluid flow rate.

[0055] S1.2: Input the test parameter table into the microfluidic chip testing instrument, perform serialized tests according to the order of the test parameter table, and generate time-series images of the demulsification process and adhesion force signals.

[0056] The test parameter table is loaded into the microfluidic chip tester. The control program of the microfluidic chip tester automatically executes the serialized test according to the order of the test parameter table entries. Specifically, the injection pump and flow controller of the microfluidic chip tester pump emulsified asphalt and release agent according to the fluid flow rate and volume ratio in the test parameter table to form a stable two-phase flow in the observation chamber of the microfluidic chip tester. At the same time, the high-speed camera of the microfluidic chip tester continuously records the time sequence image of the demulsification process, and the adhesion force signal is collected by the micro-force sensor.

[0057] S1.3: Perform image processing and analysis on the time-series images of the demulsification process to generate demulsification kinetic curves.

[0058] Image processing is performed on the time-series image of the demulsification process. Specifically, a two-dimensional Gaussian kernel is slid pixel by pixel on the time-series image of the demulsification process, and the weighted average of the neighboring pixels of each pixel in the time-series image of the demulsification process is calculated. The weighted average is used to replace the pixels in the time-series image of the demulsification process, thereby suppressing noise while preserving the characteristics of the demulsification interface, resulting in a denoised time-series image of the demulsification process. A histogram equalization algorithm is used to perform gray-level histogram statistics on the denoised time-series image of the demulsification process, and the pixel distribution probability of each gray level is obtained from the gray-level histogram. Starting from the lowest gray level, the pixel distribution probability of each gray level is accumulated sequentially to gradually construct a probability accumulation sequence from low to high. The probability accumulation sequence is used to perform nonlinear processing on the gray values ​​of the denoised time-series image of the demulsification process. The process involves mapping the data to generate a preprocessed time-series image of the demulsification process. This preprocessed image is characterized by uniform grayscale distribution and significantly enhanced contrast. The image is then binarized using an image segmentation algorithm to generate a binary image. An edge detection algorithm is applied to calculate the gradient of the binary image, obtaining sub-pixel precision edge coordinates. Using the physical calibration scale of the microfluidic chip's observation cavity, the edge coordinates are converted into actual physical dimensions, and the vertical distance between the upper and lower edges of these actual physical dimensions is calculated to generate thickness parameters for the demulsification interface. These thickness parameters are then sorted by timestamps, and a nonlinear least squares method is used to transform the sorted thickness parameters into a continuous curve. The curve parameters are automatically optimized to generate a demulsification dynamics curve.

[0059] S1.4: Perform signal processing and feature extraction on the adhesion force signal to generate a basic adhesion force dataset.

[0060] A Butterworth low-pass filter is used to smooth and denoise the adhesion force signal, generating a denoised adhesion force signal. A peak detection algorithm is used to automatically identify and extract the maximum adhesion force value and adhesion work value in each adhesion peeling cycle from the denoised adhesion force signal. The arithmetic mean of all the maximum adhesion force values ​​and adhesion work values ​​measured multiple times in the serialization experiment is used to obtain the average maximum adhesion force and average adhesion work. The average maximum adhesion force and average adhesion work are integrated and structured into a basic adhesion force dataset.

[0061] It should be noted that the average maximum adhesion force and average adhesion work in the basic adhesion force dataset are clearly correlated. The test parameter table shows the material combination pairs and the corresponding volume ratios and fluid velocities for the material combination pairs.

[0062] S2: Merge the demulsification process parameters in the demulsification kinetic curve with the basic adhesion force dataset to generate a demulsification adhesion coupling dataset. Construct a multiphysics simulation model and use the demulsification adhesion coupling dataset for inversion and calibration to generate a calibrated digital twin model.

[0063] S2.1: Perform curve fitting and parameter extraction on the demulsification kinetic curve to obtain the parameters of the demulsification process.

[0064] Curve fitting and parameter extraction are performed on the demulsification kinetic curve. Specifically, the nonlinear least squares method is used to fit the demulsification kinetic curve to generate a mathematical expression for the demulsification kinetic curve. The time required for the demulsification kinetic curve to reach stability, the demulsification rate constant, and the demulsification half-life are read from the mathematical expression of the demulsification kinetic curve and integrated and packaged into demulsification process parameters.

[0065] The mathematical expression for the demulsification kinetic curve is:

[0066] ;

[0067] in, This refers to the time required for the demulsification process to take place. In time Thickness parameters of the demulsification interface. This is the initial stage of the demulsification process, i.e. The thickness parameter of the demulsification interface when the value is 0. This refers to the thickness parameter of the demulsification interface when the demulsification process reaches a steady state. The demulsification rate constant is obtained by... and The solution is obtained by fitting using the nonlinear least squares method.

[0068] S2.2: Combine the demulsification process parameters and the basic adhesion force dataset to generate a demulsification adhesion coupling dataset.

[0069] The demulsification process parameters and the basic adhesion force dataset are associated and combined. Specifically, the demulsification process parameters and the basic adhesion force dataset obtained in the same serialization experiment are precisely paired and linked. The successfully paired demulsification rate constant, demulsification half-life, average maximum adhesion force and average adhesion work are merged into a single record. All records are summarized and arranged to generate a demulsification adhesion coupling dataset.

[0070] S2.3: Combining Fick's second law and Navier-Stokes equations, a multiphysics coupling framework is constructed; geometric modeling and mesh generation are performed on the physical dimensions of the observation cavity of the multiphysics coupling framework and the microfluidic chip test instrument to generate a multiphysics computational mesh; boundary identification is performed on the physical dimensions of the observation cavity of the microfluidic chip test instrument to obtain boundary conditions; parameter matching is performed on different types of emulsified asphalt and release agents to obtain initial material parameters; boundary conditions and initial material parameters are applied to the multiphysics coupling framework and the multiphysics computational mesh to construct a multiphysics simulation model.

[0071] By using the water diffusion flux described by Fick's second law as the source term in the Navier-Stokes equations, the influence of water evaporation on fluid velocity is reflected. The velocity field variable is explicitly introduced into Fick's second law as a convection term, reflecting the transport effect of fluid velocity on water diffusion. This yields a two-way coupling relationship between Fick's second law and the Navier-Stokes equations. Cross-constitutive equations are then defined through this two-way coupling relationship. For example, by introducing the water concentration field variable from Fick's second law into the constitutive relation of the Navier-Stokes equations, fluid viscosity becomes a function of water concentration, forming a closed set of equations describing the strong coupling effect of water diffusion, fluid flow, and interface evolution. This completes the construction of the multiphysics coupling framework.

[0072] The physical dimensions of the observation cavity of the microfluidic chip testing instrument are imported into a geometric modeling tool, such as a CAD modeler, to generate a three-dimensional digital geometric entity. An unstructured tetrahedral mesh is used to discretize the three-dimensional digital geometric entity, generating a multiphysics computational mesh suitable for complex boundaries. Boundary types are defined using the physical dimensions of the observation cavity of the microfluidic chip testing instrument. Specifically, the walls of the observation cavity are set as no-slip wall boundaries, the inlet as a velocity inlet boundary, and the outlet as a pressure outlet boundary. The no-slip wall boundary, velocity inlet boundary, and pressure outlet boundary are used as boundary conditions. Initial density, initial viscosity, and moisture diffusion coefficient are obtained from different types of emulsified asphalt and release agents through physical experiments and historical project data, and used as initial material parameters. The boundary conditions and initial material parameters are assigned to the corresponding boundaries and regions of the multiphysics computational mesh. The multiphysics computational mesh with assigned boundary conditions and initial material parameters, along with the multiphysics coupling framework, are imported into a finite element solver to complete the construction of the multiphysics simulation model.

[0073] S2.4: Input the demulsification and adhesion coupling dataset into the multiphysics simulation model for inversion and calibration, and generate a calibrated digital twin model.

[0074] The demulsification-adhesion coupling dataset is input into a multiphysics simulation model for inversion and calibration. Specifically, the demulsification process parameters and basic adhesion force data in the dataset are used as calibration target values ​​and loaded into the multiphysics simulation model. Optimization algorithms (such as gradient descent and genetic algorithms) are used to automatically adjust the key physical parameters of the multiphysics simulation model. These key physical parameters include, but are not limited to, the viscosity time-varying function coefficient and the interfacial energy density parameter. The multiphysics simulation model performs simulation calculations based on boundary conditions and the adjusted key physical parameters, outputting predicted values ​​of the demulsification kinetic curve and adhesion force. When the root mean square error between the predicted values ​​of the demulsification kinetic curve and adhesion force and the experimental measurements in the demulsification-adhesion coupling dataset is minimized, the parameter combination of the multiphysics simulation model at this moment is recorded, and the parameter combination is fixed in the multiphysics simulation model to generate a high-precision digital twin model calibrated with experimental data.

[0075] S3: Acquire construction environment data and input it into the calibrated digital twin model for automatic multiple simulation calculations to predict the wheel adhesion risk index of different construction schemes.

[0076] S3.1: Obtain temperature, humidity, and traffic waiting time, combine and arrange them to generate a construction plan parameter table, and at the same time obtain forecast environmental data.

[0077] Temperature, humidity, and traffic waiting time are determined based on engineering experience and construction specifications. An enumeration method is used to generate a parameter table for the construction scheme containing all possible combinations. At the same time, the ambient temperature and humidity forecast values ​​for the target construction day are obtained by reading weather forecast files as forecast environmental data.

[0078] It should be noted that the interval step for traffic waiting time is usually 30 minutes. This interval step is set based on engineering experience, construction specifications, and statistical analysis of historical data. Using a 30-minute interval step balances testing accuracy and practical feasibility. A shorter interval step would result in too many test points, increasing the burden of simulation calculations, while a longer interval step would not be able to fully capture the key changes during the demulsification process. Using a 30-minute interval step effectively covers the typical trends in curing time. On-site, the focus is on the risk of traffic opening between 60 and 180 minutes after emulsified asphalt application. Based on the characteristics of the demulsification process and historical data, a 60-minute interval is selected. After the emulsified asphalt is sprayed, the demulsification process usually occurs rapidly within the first 60 minutes, with a high risk of wheel sticking. After 60 minutes, the demulsification gradually stabilizes, but material residues may still exist until 180 minutes (such as rebound caused by humidity changes). The period from 60 minutes to 180 minutes covers the critical period from high to low risk, which can capture most risk scenarios and avoid omissions. The options for obtaining the passage waiting time through the interval step are 60 minutes, 90 minutes, 120 minutes, 150 minutes and 180 minutes. Then, the five different passage waiting times are combined with different temperatures and humidity to generate a parameter table of all possible combinations of construction schemes.

[0079] S3.2: Input the construction scheme parameter table and the forecast environmental data into the calibrated digital twin model. The calibrated digital twin model automatically traverses all construction schemes in the construction scheme parameter table, performs multiple simulation calculations on the forecast environmental data and all construction schemes, and outputs the wheel adhesion risk index of different construction schemes.

[0080] The construction scheme parameter table and the predicted environmental data are input into the calibrated digital twin model. The control program of the digital twin model reads each construction scheme entry in the construction scheme parameter table in sequence, and binds the passage waiting time in each construction scheme entry with the temperature and humidity in the predicted environmental data as the initial conditions for the current simulation. The digital twin model performs high-fidelity simulation calculations on the initial conditions. The multiphysics coupling framework inside the digital twin model solves the initial conditions synchronously. Specifically, it calculates the real-time influence of temperature and humidity on the water evaporation rate using Fick's second law to obtain the change in water diffusion flux. The asphalt phase viscosity is updated by the change in water diffusion flux. The updated asphalt phase viscosity is substituted into the Navier-Stokes equations to solve the fluid momentum field and obtain the flow velocity and pressure distribution. The entire demulsification process is gradually deduced by the flow velocity and pressure distribution in minutes, the interface thickness and adhesion force are recorded, and the maximum adhesion force and adhesion work are extracted. At the end of the high-fidelity simulation, the wheel adhesion risk index under a specific construction scheme is synthesized by the risk index formula. The high-fidelity simulation calculation is repeated until all construction scheme entries in the construction scheme parameter table are traversed, and the wheel adhesion risk index under different construction schemes is output.

[0081] S4: The decision algorithm is used to screen the wheel adhesion risk index of different construction schemes, generate the construction scheme with the lowest wheel adhesion risk index, extract the wheel adhesion risk index time series data from the construction scheme with the lowest wheel adhesion risk index, and perform time series analysis on the wheel adhesion risk index time series data to generate a safe passage time window.

[0082] S4.1: Compare and rank the wheel adhesion risk index of different construction schemes using a sorting algorithm, and obtain the construction scheme with the lowest wheel adhesion risk index.

[0083] The wheel adhesion risk index of different construction schemes is compared and ranked by a sorting algorithm. Specifically, the wheel adhesion risk index of different construction schemes is loaded into the computer memory that executes the sorting algorithm. The quick sort algorithm is used to sort all wheel adhesion risk indices in ascending order. The construction scheme (including temperature, humidity and passage waiting time) corresponding to the first record in the sorted sequence is extracted, which is the construction scheme with the lowest wheel adhesion risk index.

[0084] S4.2: Perform index queries and data retrieval on the construction scheme with the lowest wheel adhesion risk index to generate time series data of wheel adhesion risk index.

[0085] Using the unique identifier of the construction scheme with the lowest wheel adhesion risk index as the search key, an index query is performed on the wheel adhesion risk index of all different construction schemes output by high-fidelity simulation calculations to locate the result corresponding to the construction scheme with the lowest wheel adhesion risk index, and the complete sequence value of the wheel adhesion risk index over time is extracted, which is the time series data of wheel adhesion risk index.

[0086] S4.3: Based on historical experimental data, set a safety threshold for the sticking wheel risk index, traverse the time series data of the sticking wheel risk index, and identify the start time when the sticking wheel risk index first falls below the safety threshold and the end time when it finally exceeds the safety threshold.

[0087] Based on the correlation between the wheel sticking risk index and actual wheel sticking accidents in a large amount of historical experimental data, statistical analysis methods are used, such as determining the quantiles at a specific confidence level, to set a safe threshold for the wheel sticking risk index. The value range is usually 0.05-0.15. By statistically analyzing historical data and considering safety margins, the range of the safe threshold for the wheel sticking risk index is set. The range of 0.05-0.15 corresponds to an acceptable risk level where the incidence of wheel sticking accidents in the field is less than 5%, which improves the scientific nature and repeatability of decision-making and avoids the risks of making decisions based on intuition. By traversing each time point in the time series data of the wheel sticking risk index, and through logical comparison, the time point when the wheel sticking risk index first changes from above the safe threshold to continuously below the safe threshold is identified and recorded as the start time. The process continues to the end of the time series data of the wheel sticking risk index to identify the time point when the wheel sticking risk index last changes from below the safe threshold to above the safe threshold and is recorded as the end time.

[0088] S4.4: Use the time interval between the start time and the end time as the safe passage time window.

[0089] By using the start and end times as the upper and lower boundaries of the time interval, and by calculating the time difference between the end and start times and confirming continuity, the specific time range of the safe passage time window can be obtained.

[0090] S5: Integrate the construction scheme with the lowest roller adhesion risk index with the safe passage time window to generate intelligent decision suggestions.

[0091] S5.1: Perform parameter analysis on the construction scheme with the lowest sticking risk index to generate the optimal construction scheme parameter set.

[0092] The construction scheme with the lowest risk of wheel adhesion is analyzed by parameters. Specifically, the temperature, humidity and passage waiting time in the construction scheme with the lowest risk of wheel adhesion are read, and the fields are extracted and encapsulated to generate the parameter set of the optimal construction scheme.

[0093] S5.2: The optimal construction scheme parameter set and safe passage time window are structurally integrated to generate intelligent decision suggestions.

[0094] By reading temperature, humidity, and passage waiting time from the optimal construction plan parameter set, and extracting the start and end times from the safe passage time window, the temperature, humidity, passage waiting time, start and end times are combined and packaged according to the report format to generate intelligent decision suggestions that include recommended construction plan parameters and safe passage time windows.

[0095] This embodiment also provides a computer device applicable to the method for evaluating the adhesion effect of emulsified asphalt on wheels, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for evaluating the adhesion effect of emulsified asphalt on wheels as proposed in the above embodiment.

[0096] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0097] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for evaluating the adhesion effect of emulsified asphalt as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0098] In summary, this invention achieves holographic quantification of the demulsification process from morphological changes to mechanical responses by synchronously acquiring time-series images and mechanical signals. Through sequential experimental design, it significantly improves data acquisition efficiency. By inverting and calibrating the interface energy density through experimental data, it reduces behavioral errors between simulation models and real materials. By automatically traversing all possible construction schemes, it enhances the reliability of the schemes. Based on trend analysis of time series, it identifies risk rebound points in advance, enabling proactive prevention and control. By generating decision suggestions based on specific materials and environments, it improves the consistency of construction quality.

[0099] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating the adhesion effect of emulsified asphalt to wheels, characterized in that: include, Obtain the test parameter table, input the test parameter table into the microfluidic chip tester for high-throughput testing, and output the demulsification kinetic curve and basic adhesion force dataset; The demulsification process parameters in the demulsification kinetic curve are merged with the basic adhesion force dataset to generate a demulsification adhesion coupling dataset. A multiphysics simulation model is constructed and the demulsification adhesion coupling dataset is used for inversion and calibration to generate a calibrated digital twin model. The process involves constructing a multiphysics simulation model and using a demulsification and adhesion coupling dataset for inversion and calibration to generate a calibrated digital twin model. The specific steps are as follows: By combining Fick's second law with Navier-Stokes' equations, a multiphysics coupling framework is constructed. Geometric modeling and mesh generation were performed on the physical dimensions of the observation cavity of the multiphysics coupling framework and microfluidic chip experimental instrument to generate a multiphysics computational mesh; Boundary identification of the physical dimensions of the observation cavity of the microfluidic chip test instrument is performed to obtain boundary conditions. Parameter matching is performed on different types of emulsified asphalt and release agents to obtain initial material parameters. Boundary conditions and initial material parameters are applied to the multiphysics coupling framework and the multiphysics computational grid to construct a multiphysics simulation model; The demulsification and adhesion coupling dataset is input into the multiphysics simulation model for inversion and calibration, generating a calibrated digital twin model. The construction environment data is acquired and input into the calibrated digital twin model for automatic multiple simulation calculations to predict the wheel adhesion risk index of different construction schemes; The decision algorithm is used to screen the wheel adhesion risk index of different construction schemes, generate the construction scheme with the lowest wheel adhesion risk index, extract the wheel adhesion risk index time series data from the construction scheme with the lowest wheel adhesion risk index, and perform time series analysis on the wheel adhesion risk index time series data to generate a safe passage time window. By integrating the construction scheme with the lowest risk of wheel adhesion and the safe passage time window, intelligent decision-making suggestions are generated.

2. The method for evaluating the adhesion effect of emulsified asphalt to wheels as described in claim 1, characterized in that: The specific steps for outputting the demulsification kinetics curve and the basic adhesion force dataset are as follows. Different types of emulsified asphalt and release agents were obtained and combined to generate a test parameter table; Input the test parameter table into the microfluidic chip testing instrument, perform serialized tests according to the order of the test parameter table, and generate time-series images of the demulsification process and adhesion force signals; Image processing and analysis are performed on time-series images of the demulsification process to generate demulsification dynamics curves; Signal processing and feature extraction are performed on the adhesion force signal to generate a basic adhesion force dataset.

3. The method for evaluating the adhesion effect of emulsified asphalt to wheels as described in claim 1, characterized in that: The steps for merging the demulsification process parameters from the demulsification kinetics curve with the basic adhesion force dataset to generate a demulsification adhesion coupling dataset are as follows. Curve fitting and parameter extraction were performed on the demulsification kinetics curve to obtain parameters of the demulsification process. The demulsification process parameters and the basic adhesion force dataset are correlated and combined to generate a demulsification adhesion coupling dataset.

4. The method for evaluating the adhesion effect of emulsified asphalt to wheels as described in claim 1, characterized in that: The specific steps for predicting the wheel adhesion risk index for different construction schemes are as follows: The system acquires temperature, humidity, and traffic waiting time, combines and arranges them to generate a construction plan parameter table, and simultaneously acquires forecast environmental data. The construction scheme parameter table and the forecast environmental data are input into the calibrated digital twin model. The calibrated digital twin model automatically traverses all construction schemes in the construction scheme parameter table, performs multiple simulation calculations on the forecast environmental data and all construction schemes, and outputs the wheel adhesion risk index of different construction schemes.

5. The method for evaluating the adhesion effect of emulsified asphalt to wheels as described in claim 1, characterized in that: The specific steps for extracting the time series data of the wheel adhesion risk index from the construction scheme with the lowest wheel adhesion risk index are as follows: The wheel adhesion risk index of different construction schemes is compared and ranked by a sorting algorithm to obtain the construction scheme with the lowest wheel adhesion risk index. The construction scheme with the lowest wheel adhesion risk index is indexed and retrieved to generate time series data of wheel adhesion risk index.

6. The method for evaluating the adhesion effect of emulsified asphalt to wheels as described in claim 1, characterized in that: The specific steps for performing time series analysis on the time series data of the sticky wheel risk index to generate a safe passage time window are as follows. The safety threshold for the sticking wheel risk index was set based on historical experimental data; Traverse the time series data of the sticky wheel risk index to identify the start time when the sticky wheel risk index first falls below the safe threshold and the end time when it last exceeds the safe threshold. The time interval between the start time and the end time is used as the safe passage time window.

7. The method for evaluating the adhesion effect of emulsified asphalt to wheels as described in claim 1, characterized in that: The process of integrating the construction plan with the lowest wheel adhesion risk index and the safe passage time window to generate intelligent decision-making suggestions involves the following steps: The construction scheme with the lowest roller adhesion risk index is analyzed to generate the optimal construction scheme parameter set. The optimal construction plan parameter set and safe passage time window are structurally integrated to generate intelligent decision suggestions.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for evaluating the adhesion effect of emulsified asphalt as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for evaluating the adhesion effect of emulsified asphalt as described in any one of claims 1 to 7.

Images